2020 Census Data was sourced from Census.Gov. We filtered this data to show the number of households, families, and non-family households for each zipcode. From this dataset we display the Median income, mean income, and the Percent of Income allocated to Housing Cost for each type of household.
For our analysis we devised an unsupervised K-Means Machine Learning model. The dataset inputted into the model includes median income, mean income, and percent of income allocated to housing costs for households, families, and non-family households. Additionally, we identified how many amenties are found in each zipcode, including hospitals, schools, markets, parks, and public transportation.
The zipcodes identified in the K-Means cluster model as being in Class 4 should be first priority when considering where to place low-income affordable housing units. These areas are highlighted in the map on the right and infomration on these areas can be found in the table below.
| Zip Code | Median Income (dollars) | Percent of Income allocated to Housing Cost | Class |
|---|---|---|---|
| 91910 | 70283.0 | 46.8 | 4 |
| 91911 | 65074.0 | 50.9 | 4 |
| 91950 | 48359.0 | 44.8 | 4 |
| 91977 | 71396.0 | 44.8 | 4 |
| 92020 | 61830.0 | 43.0 | 4 |
| 92021 | 60510.0 | 43.7 | 4 |
| 92105 | 48072.0 | 47.2 | 4 |
| 92113 | 43958.0 | 41.9 | 4 |
| 92114 | 73090.0 | 51.2 | 4 |
| 92115 | 56137.0 | 36.2 | 4 |
| 92126 | 99376.0 | 33.0 | 4 |
| 92154 | 70846.0 | 45.2 | 4 |
The zipcodes identified in the K-Means cluster model as being in Class 0 would be second priority when considering where to place low-income affordable housing units. These areas are highlighted in the map on the left and infomration on these areas can be found in the table below.
| Zip Code | Median Income (dollars) | Percent of Income allocated to Housing Cost | Class |
|---|---|---|---|
| 91932 | 59795.0 | 39.6 | 0 |
| 91941 | 94111.0 | 39.4 | 0 |
| 91942 | 66551.0 | 34.1 | 0 |
| 91945 | 67236.0 | 44.1 | 0 |
| 92008 | 86046.0 | 25.8 | 0 |
| 92019 | 85067.0 | 40.1 | 0 |
| 92025 | 56866.0 | 40.4 | 0 |
| 92026 | 76534.0 | 36.3 | 0 |
| 92027 | 70009.0 | 43.5 | 0 |
| 92028 | 80775.0 | 37.1 | 0 |
| 92040 | 83692.0 | 40.3 | 0 |
| 92054 | 63355.0 | 31.2 | 0 |
| 92056 | 85047.0 | 34.3 | 0 |
| 92057 | 81339.0 | 33.8 | 0 |
| 92058 | 57213.0 | 31.7 | 0 |
| 92065 | 100645.0 | 34.1 | 0 |
| 92069 | 77618.0 | 35.7 | 0 |
| 92071 | 85751.0 | 36.2 | 0 |
| 92078 | 91564.0 | 33.6 | 0 |
| 92081 | 80584.0 | 30.3 | 0 |
| 92083 | 68551.0 | 32.1 | 0 |
| 92084 | 77970.0 | 39.8 | 0 |
| 92102 | 54862.0 | 35.3 | 0 |
| 92107 | 84190.0 | 35.0 | 0 |
| 92108 | 80572.0 | 26.8 | 0 |
| 92110 | 77579.0 | 33.7 | 0 |
| 92111 | 77249.0 | 40.8 | 0 |
| 92117 | 91347.0 | 34.9 | 0 |
| 92120 | 102597.0 | 31.4 | 0 |
| 92123 | 90602.0 | 35.6 | 0 |
| 92124 | 94485.0 | 30.3 | 0 |
| 92139 | 75576.0 | 52.7 | 0 |
| 92173 | 48967.0 | 44.1 | 0 |
| 92672 | 89029.0 | 35.7 | 0 |